Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations32588
Missing cells44028
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.8 MiB
Average record size in memory1.3 KiB

Variable types

Numeric6
Text9
DateTime2
Categorical4
URL2

Alerts

highest_market_value_in_eur is highly overall correlated with market_value_in_eurHigh correlation
last_season is highly overall correlated with market_value_in_eur and 1 other fieldsHigh correlation
market_value_in_eur is highly overall correlated with highest_market_value_in_eur and 1 other fieldsHigh correlation
player_id is highly overall correlated with last_seasonHigh correlation
position is highly overall correlated with sub_positionHigh correlation
sub_position is highly overall correlated with positionHigh correlation
first_name has 2062 (6.3%) missing values Missing
country_of_birth has 2809 (8.6%) missing values Missing
city_of_birth has 2465 (7.6%) missing values Missing
country_of_citizenship has 383 (1.2%) missing values Missing
foot has 2540 (7.8%) missing values Missing
height_in_cm has 2263 (6.9%) missing values Missing
contract_expiration_date has 12098 (37.1%) missing values Missing
agent_name has 16052 (49.3%) missing values Missing
market_value_in_eur has 1564 (4.8%) missing values Missing
highest_market_value_in_eur has 1564 (4.8%) missing values Missing
player_id has unique values Unique
url has unique values Unique

Reproduction

Analysis started2025-03-12 19:12:36.842620
Analysis finished2025-03-12 19:12:42.554519
Duration5.71 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

player_id
Real number (ℝ)

High correlation  Unique 

Distinct32588
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347978.22
Minimum10
Maximum1380876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.7 KiB
2025-03-12T21:12:42.771307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile19354.7
Q1107936.5
median283615
Q3529667
95-th percentile924831.85
Maximum1380876
Range1380866
Interquartile range (IQR)421730.5

Descriptive statistics

Standard deviation284104.04
Coefficient of variation (CV)0.81644203
Kurtosis0.072423893
Mean347978.22
Median Absolute Deviation (MAD)197606
Skewness0.88322325
Sum1.1339914 × 1010
Variance8.0715108 × 1010
MonotonicityStrictly increasing
2025-03-12T21:12:42.847510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1
 
< 0.1%
420921 1
 
< 0.1%
421726 1
 
< 0.1%
421710 1
 
< 0.1%
421637 1
 
< 0.1%
421636 1
 
< 0.1%
421424 1
 
< 0.1%
421319 1
 
< 0.1%
421284 1
 
< 0.1%
421224 1
 
< 0.1%
Other values (32578) 32578
> 99.9%
ValueCountFrequency (%)
10 1
< 0.1%
26 1
< 0.1%
65 1
< 0.1%
77 1
< 0.1%
80 1
< 0.1%
109 1
< 0.1%
123 1
< 0.1%
132 1
< 0.1%
162 1
< 0.1%
215 1
< 0.1%
ValueCountFrequency (%)
1380876 1
< 0.1%
1380311 1
< 0.1%
1378362 1
< 0.1%
1375876 1
< 0.1%
1369057 1
< 0.1%
1367128 1
< 0.1%
1365020 1
< 0.1%
1364960 1
< 0.1%
1358448 1
< 0.1%
1358447 1
< 0.1%

first_name
Text

Missing 

Distinct7024
Distinct (%)23.0%
Missing2062
Missing (%)6.3%
Memory size2.0 MiB
2025-03-12T21:12:43.045365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length19
Median length17
Mean length5.9755618
Min length2

Characters and Unicode

Total characters182410
Distinct characters105
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4445 ?
Unique (%)14.6%

Sample

1st rowMiroslav
2nd rowRoman
3rd rowDimitar
4th rowTom
5th rowChristoph
ValueCountFrequency (%)
david 219
 
0.7%
ivan 149
 
0.5%
daniel 134
 
0.4%
marco 133
 
0.4%
georgios 126
 
0.4%
aleksandr 123
 
0.4%
lucas 122
 
0.4%
luca 117
 
0.4%
thomas 115
 
0.4%
michael 114
 
0.4%
Other values (6765) 29715
95.6%
2025-03-12T21:12:43.314560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20379
 
11.2%
i 15063
 
8.3%
n 13668
 
7.5%
e 13659
 
7.5%
o 12554
 
6.9%
r 11912
 
6.5%
l 8680
 
4.8%
s 8383
 
4.6%
t 5826
 
3.2%
m 5017
 
2.8%
Other values (95) 67269
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20379
 
11.2%
i 15063
 
8.3%
n 13668
 
7.5%
e 13659
 
7.5%
o 12554
 
6.9%
r 11912
 
6.5%
l 8680
 
4.8%
s 8383
 
4.6%
t 5826
 
3.2%
m 5017
 
2.8%
Other values (95) 67269
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20379
 
11.2%
i 15063
 
8.3%
n 13668
 
7.5%
e 13659
 
7.5%
o 12554
 
6.9%
r 11912
 
6.5%
l 8680
 
4.8%
s 8383
 
4.6%
t 5826
 
3.2%
m 5017
 
2.8%
Other values (95) 67269
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20379
 
11.2%
i 15063
 
8.3%
n 13668
 
7.5%
e 13659
 
7.5%
o 12554
 
6.9%
r 11912
 
6.5%
l 8680
 
4.8%
s 8383
 
4.6%
t 5826
 
3.2%
m 5017
 
2.8%
Other values (95) 67269
36.9%
Distinct23789
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-03-12T21:12:43.460385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.1138149
Min length2

Characters and Unicode

Total characters231825
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20183 ?
Unique (%)61.9%

Sample

1st rowKlose
2nd rowWeidenfeller
3rd rowBerbatov
4th rowLúcio
5th rowStarke
ValueCountFrequency (%)
van 253
 
0.7%
de 218
 
0.6%
silva 99
 
0.3%
el 82
 
0.2%
garcía 71
 
0.2%
santos 59
 
0.2%
lópez 58
 
0.2%
rodríguez 57
 
0.2%
der 57
 
0.2%
gonzález 52
 
0.2%
Other values (23294) 33462
97.1%
2025-03-12T21:12:43.715653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24201
 
10.4%
e 19255
 
8.3%
o 17274
 
7.5%
i 16947
 
7.3%
n 14938
 
6.4%
r 14619
 
6.3%
l 10946
 
4.7%
s 10878
 
4.7%
u 7642
 
3.3%
t 7476
 
3.2%
Other values (105) 87649
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24201
 
10.4%
e 19255
 
8.3%
o 17274
 
7.5%
i 16947
 
7.3%
n 14938
 
6.4%
r 14619
 
6.3%
l 10946
 
4.7%
s 10878
 
4.7%
u 7642
 
3.3%
t 7476
 
3.2%
Other values (105) 87649
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24201
 
10.4%
e 19255
 
8.3%
o 17274
 
7.5%
i 16947
 
7.3%
n 14938
 
6.4%
r 14619
 
6.3%
l 10946
 
4.7%
s 10878
 
4.7%
u 7642
 
3.3%
t 7476
 
3.2%
Other values (105) 87649
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24201
 
10.4%
e 19255
 
8.3%
o 17274
 
7.5%
i 16947
 
7.3%
n 14938
 
6.4%
r 14619
 
6.3%
l 10946
 
4.7%
s 10878
 
4.7%
u 7642
 
3.3%
t 7476
 
3.2%
Other values (105) 87649
37.8%

name
Text

Distinct31880
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
2025-03-12T21:12:43.889207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length29
Mean length13.647999
Min length2

Characters and Unicode

Total characters444761
Distinct characters124
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31356 ?
Unique (%)96.2%

Sample

1st rowMiroslav Klose
2nd rowRoman Weidenfeller
3rd rowDimitar Berbatov
4th rowLúcio
5th rowTom Starke
ValueCountFrequency (%)
van 254
 
0.4%
david 229
 
0.3%
de 218
 
0.3%
lucas 157
 
0.2%
ivan 154
 
0.2%
daniel 139
 
0.2%
marco 137
 
0.2%
thomas 135
 
0.2%
georgios 126
 
0.2%
aleksandr 123
 
0.2%
Other values (28923) 63863
97.4%
2025-03-12T21:12:44.128042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 44580
 
10.0%
32947
 
7.4%
e 32914
 
7.4%
i 32010
 
7.2%
o 29828
 
6.7%
n 28606
 
6.4%
r 26531
 
6.0%
l 19626
 
4.4%
s 19261
 
4.3%
t 13302
 
3.0%
Other values (114) 165156
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 444761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 44580
 
10.0%
32947
 
7.4%
e 32914
 
7.4%
i 32010
 
7.2%
o 29828
 
6.7%
n 28606
 
6.4%
r 26531
 
6.0%
l 19626
 
4.4%
s 19261
 
4.3%
t 13302
 
3.0%
Other values (114) 165156
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 444761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 44580
 
10.0%
32947
 
7.4%
e 32914
 
7.4%
i 32010
 
7.2%
o 29828
 
6.7%
n 28606
 
6.4%
r 26531
 
6.0%
l 19626
 
4.4%
s 19261
 
4.3%
t 13302
 
3.0%
Other values (114) 165156
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 444761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 44580
 
10.0%
32947
 
7.4%
e 32914
 
7.4%
i 32010
 
7.2%
o 29828
 
6.7%
n 28606
 
6.4%
r 26531
 
6.0%
l 19626
 
4.4%
s 19261
 
4.3%
t 13302
 
3.0%
Other values (114) 165156
37.1%

last_season
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.3561
Minimum2012
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.7 KiB
2025-03-12T21:12:44.191341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12016
median2020
Q32023
95-th percentile2024
Maximum2024
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.9620232
Coefficient of variation (CV)0.001962023
Kurtosis-1.1890737
Mean2019.3561
Median Absolute Deviation (MAD)3
Skewness-0.40362871
Sum65806778
Variance15.697628
MonotonicityNot monotonic
2025-03-12T21:12:44.243299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2024 6426
19.7%
2023 4039
12.4%
2022 2638
8.1%
2021 2336
 
7.2%
2018 2270
 
7.0%
2020 2169
 
6.7%
2013 2115
 
6.5%
2016 1918
 
5.9%
2017 1798
 
5.5%
2019 1783
 
5.5%
Other values (3) 5096
15.6%
ValueCountFrequency (%)
2012 1679
5.2%
2013 2115
6.5%
2014 1704
5.2%
2015 1713
5.3%
2016 1918
5.9%
2017 1798
5.5%
2018 2270
7.0%
2019 1783
5.5%
2020 2169
6.7%
2021 2336
7.2%
ValueCountFrequency (%)
2024 6426
19.7%
2023 4039
12.4%
2022 2638
8.1%
2021 2336
 
7.2%
2020 2169
 
6.7%
2019 1783
 
5.5%
2018 2270
 
7.0%
2017 1798
 
5.5%
2016 1918
 
5.9%
2015 1713
 
5.3%

current_club_id
Real number (ℝ)

Distinct437
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4807.0534
Minimum3
Maximum110302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.7 KiB
2025-03-12T21:12:44.311550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile41
Q1403
median1071
Q33060
95-th percentile26459
Maximum110302
Range110299
Interquartile range (IQR)2657

Descriptive statistics

Standard deviation11560.679
Coefficient of variation (CV)2.4049408
Kurtosis21.835704
Mean4807.0534
Median Absolute Deviation (MAD)923
Skewness4.3625782
Sum1.5665226 × 108
Variance1.3364929 × 108
MonotonicityNot monotonic
2025-03-12T21:12:44.379171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2553 185
 
0.6%
987 182
 
0.6%
2759 180
 
0.6%
511 178
 
0.5%
2578 166
 
0.5%
126 166
 
0.5%
589 164
 
0.5%
252 163
 
0.5%
800 158
 
0.5%
6676 156
 
0.5%
Other values (427) 30890
94.8%
ValueCountFrequency (%)
3 74
0.2%
4 37
 
0.1%
5 93
0.3%
6 27
 
0.1%
10 28
 
0.1%
11 75
0.2%
12 123
0.4%
13 91
0.3%
15 75
0.2%
16 64
0.2%
ValueCountFrequency (%)
110302 28
 
0.1%
86209 24
 
0.1%
85465 28
 
0.1%
83678 36
 
0.1%
75231 23
 
0.1%
71985 26
 
0.1%
68608 51
0.2%
63007 45
0.1%
61825 80
0.2%
60949 100
0.3%
Distinct31840
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-03-12T21:12:44.504170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length35
Median length30
Mean length13.645913
Min length4

Characters and Unicode

Total characters444693
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31292 ?
Unique (%)96.0%

Sample

1st rowmiroslav-klose
2nd rowroman-weidenfeller
3rd rowdimitar-berbatov
4th rowlucio
5th rowtom-starke
ValueCountFrequency (%)
paulinho 13
 
< 0.1%
joao-pedro 9
 
< 0.1%
guilherme 8
 
< 0.1%
vinicius 7
 
< 0.1%
alex 7
 
< 0.1%
danilo 7
 
< 0.1%
bruninho 7
 
< 0.1%
serginho 7
 
< 0.1%
julio-cesar 7
 
< 0.1%
pedro 5
 
< 0.1%
Other values (31830) 32511
99.8%
2025-03-12T21:12:44.704373image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 51517
 
11.6%
e 36575
 
8.2%
i 34264
 
7.7%
- 33422
 
7.5%
o 32280
 
7.3%
n 30729
 
6.9%
r 29590
 
6.7%
s 24635
 
5.5%
l 22478
 
5.1%
m 16272
 
3.7%
Other values (22) 132931
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 444693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 51517
 
11.6%
e 36575
 
8.2%
i 34264
 
7.7%
- 33422
 
7.5%
o 32280
 
7.3%
n 30729
 
6.9%
r 29590
 
6.7%
s 24635
 
5.5%
l 22478
 
5.1%
m 16272
 
3.7%
Other values (22) 132931
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 444693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 51517
 
11.6%
e 36575
 
8.2%
i 34264
 
7.7%
- 33422
 
7.5%
o 32280
 
7.3%
n 30729
 
6.9%
r 29590
 
6.7%
s 24635
 
5.5%
l 22478
 
5.1%
m 16272
 
3.7%
Other values (22) 132931
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 444693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 51517
 
11.6%
e 36575
 
8.2%
i 34264
 
7.7%
- 33422
 
7.5%
o 32280
 
7.3%
n 30729
 
6.9%
r 29590
 
6.7%
s 24635
 
5.5%
l 22478
 
5.1%
m 16272
 
3.7%
Other values (22) 132931
29.9%

country_of_birth
Text

Missing 

Distinct185
Distinct (%)0.6%
Missing2809
Missing (%)8.6%
Memory size1.9 MiB
2025-03-12T21:12:44.850163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length30
Median length24
Mean length7.2366433
Min length4

Characters and Unicode

Total characters215500
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.1%

Sample

1st rowPoland
2nd rowGermany
3rd rowBulgaria
4th rowBrazil
5th rowEast Germany (GDR)
ValueCountFrequency (%)
france 2334
 
7.4%
spain 1995
 
6.3%
italy 1849
 
5.9%
england 1832
 
5.8%
germany 1653
 
5.2%
brazil 1617
 
5.1%
netherlands 1522
 
4.8%
turkey 1196
 
3.8%
portugal 1119
 
3.5%
ukraine 1020
 
3.2%
Other values (203) 15444
48.9%
2025-03-12T21:12:45.132778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 26461
 
12.3%
e 20519
 
9.5%
n 19220
 
8.9%
r 16652
 
7.7%
l 12861
 
6.0%
i 12408
 
5.8%
t 7884
 
3.7%
d 6992
 
3.2%
u 6853
 
3.2%
S 6656
 
3.1%
Other values (50) 78994
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 215500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 26461
 
12.3%
e 20519
 
9.5%
n 19220
 
8.9%
r 16652
 
7.7%
l 12861
 
6.0%
i 12408
 
5.8%
t 7884
 
3.7%
d 6992
 
3.2%
u 6853
 
3.2%
S 6656
 
3.1%
Other values (50) 78994
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 215500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 26461
 
12.3%
e 20519
 
9.5%
n 19220
 
8.9%
r 16652
 
7.7%
l 12861
 
6.0%
i 12408
 
5.8%
t 7884
 
3.7%
d 6992
 
3.2%
u 6853
 
3.2%
S 6656
 
3.1%
Other values (50) 78994
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 215500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 26461
 
12.3%
e 20519
 
9.5%
n 19220
 
8.9%
r 16652
 
7.7%
l 12861
 
6.0%
i 12408
 
5.8%
t 7884
 
3.7%
d 6992
 
3.2%
u 6853
 
3.2%
S 6656
 
3.1%
Other values (50) 78994
36.7%

city_of_birth
Text

Missing 

Distinct8578
Distinct (%)28.5%
Missing2465
Missing (%)7.6%
Memory size2.1 MiB
2025-03-12T21:12:45.275674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length56
Median length42
Mean length9.141387
Min length1

Characters and Unicode

Total characters275366
Distinct characters151
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5270 ?
Unique (%)17.5%

Sample

1st rowOpole
2nd rowDiez
3rd rowBlagoevgrad
4th rowBrasília
5th rowFreital
ValueCountFrequency (%)
de 742
 
1.9%
oblast 711
 
1.8%
london 479
 
1.2%
istanbul 341
 
0.9%
são 332
 
0.9%
region 298
 
0.8%
moskau 248
 
0.6%
amsterdam 204
 
0.5%
paulo 197
 
0.5%
san 196
 
0.5%
Other values (8974) 35216
90.4%
2025-03-12T21:12:45.529825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 29973
 
10.9%
e 22135
 
8.0%
o 19265
 
7.0%
r 18215
 
6.6%
n 18189
 
6.6%
i 17403
 
6.3%
s 12954
 
4.7%
l 12911
 
4.7%
t 10377
 
3.8%
8841
 
3.2%
Other values (141) 105103
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 275366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 29973
 
10.9%
e 22135
 
8.0%
o 19265
 
7.0%
r 18215
 
6.6%
n 18189
 
6.6%
i 17403
 
6.3%
s 12954
 
4.7%
l 12911
 
4.7%
t 10377
 
3.8%
8841
 
3.2%
Other values (141) 105103
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 275366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 29973
 
10.9%
e 22135
 
8.0%
o 19265
 
7.0%
r 18215
 
6.6%
n 18189
 
6.6%
i 17403
 
6.3%
s 12954
 
4.7%
l 12911
 
4.7%
t 10377
 
3.8%
8841
 
3.2%
Other values (141) 105103
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 275366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 29973
 
10.9%
e 22135
 
8.0%
o 19265
 
7.0%
r 18215
 
6.6%
n 18189
 
6.6%
i 17403
 
6.3%
s 12954
 
4.7%
l 12911
 
4.7%
t 10377
 
3.8%
8841
 
3.2%
Other values (141) 105103
38.2%
Distinct183
Distinct (%)0.6%
Missing383
Missing (%)1.2%
Memory size2.0 MiB
2025-03-12T21:12:45.714220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length24
Median length21
Mean length7.0979041
Min length4

Characters and Unicode

Total characters228588
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st rowGermany
2nd rowGermany
3rd rowBulgaria
4th rowBrazil
5th rowGermany
ValueCountFrequency (%)
spain 1966
 
5.8%
italy 1888
 
5.6%
france 1762
 
5.2%
brazil 1626
 
4.8%
england 1591
 
4.7%
ukraine 1494
 
4.4%
russia 1481
 
4.4%
netherlands 1445
 
4.3%
turkey 1424
 
4.2%
germany 1338
 
4.0%
Other values (202) 17677
52.5%
2025-03-12T21:12:45.966314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 28918
 
12.7%
e 22992
 
10.1%
n 19666
 
8.6%
r 19036
 
8.3%
i 14782
 
6.5%
l 13078
 
5.7%
t 8418
 
3.7%
o 7815
 
3.4%
u 7602
 
3.3%
g 6497
 
2.8%
Other values (48) 79784
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 28918
 
12.7%
e 22992
 
10.1%
n 19666
 
8.6%
r 19036
 
8.3%
i 14782
 
6.5%
l 13078
 
5.7%
t 8418
 
3.7%
o 7815
 
3.4%
u 7602
 
3.3%
g 6497
 
2.8%
Other values (48) 79784
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 28918
 
12.7%
e 22992
 
10.1%
n 19666
 
8.6%
r 19036
 
8.3%
i 14782
 
6.5%
l 13078
 
5.7%
t 8418
 
3.7%
o 7815
 
3.4%
u 7602
 
3.3%
g 6497
 
2.8%
Other values (48) 79784
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 28918
 
12.7%
e 22992
 
10.1%
n 19666
 
8.6%
r 19036
 
8.3%
i 14782
 
6.5%
l 13078
 
5.7%
t 8418
 
3.7%
o 7815
 
3.4%
u 7602
 
3.3%
g 6497
 
2.8%
Other values (48) 79784
34.9%
Distinct9295
Distinct (%)28.6%
Missing47
Missing (%)0.1%
Memory size254.7 KiB
Minimum1968-07-31 00:00:00
Maximum2009-09-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T21:12:46.107129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:46.192298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sub_position
Categorical

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing181
Missing (%)0.6%
Memory size2.2 MiB
Centre-Back
5746 
Centre-Forward
4570 
Central Midfield
3783 
Goalkeeper
3723 
Defensive Midfield
2657 
Other values (8)
11928 

Length

Max length18
Median length14
Mean length12.869442
Min length9

Characters and Unicode

Total characters417060
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentre-Forward
2nd rowGoalkeeper
3rd rowCentre-Forward
4th rowCentre-Back
5th rowGoalkeeper

Common Values

ValueCountFrequency (%)
Centre-Back 5746
17.6%
Centre-Forward 4570
14.0%
Central Midfield 3783
11.6%
Goalkeeper 3723
11.4%
Defensive Midfield 2657
8.2%
Right-Back 2371
7.3%
Left-Back 2284
 
7.0%
Attacking Midfield 2221
 
6.8%
Left Winger 2103
 
6.5%
Right Winger 2001
 
6.1%
Other values (3) 948
 
2.9%

Length

2025-03-12T21:12:46.261328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
midfield 9371
20.3%
centre-back 5746
12.5%
centre-forward 4570
9.9%
winger 4104
8.9%
central 3783
8.2%
goalkeeper 3723
 
8.1%
defensive 2657
 
5.8%
left 2474
 
5.4%
right-back 2371
 
5.1%
right 2340
 
5.1%
Other values (4) 4981
10.8%

Most occurring characters

ValueCountFrequency (%)
e 62264
14.9%
i 32673
 
7.8%
r 31542
 
7.6%
t 28248
 
6.8%
a 24698
 
5.9%
d 23550
 
5.6%
n 23319
 
5.6%
l 16877
 
4.0%
f 16786
 
4.0%
k 16583
 
4.0%
Other values (21) 140520
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 417060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 62264
14.9%
i 32673
 
7.8%
r 31542
 
7.6%
t 28248
 
6.8%
a 24698
 
5.9%
d 23550
 
5.6%
n 23319
 
5.6%
l 16877
 
4.0%
f 16786
 
4.0%
k 16583
 
4.0%
Other values (21) 140520
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 417060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 62264
14.9%
i 32673
 
7.8%
r 31542
 
7.6%
t 28248
 
6.8%
a 24698
 
5.9%
d 23550
 
5.6%
n 23319
 
5.6%
l 16877
 
4.0%
f 16786
 
4.0%
k 16583
 
4.0%
Other values (21) 140520
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 417060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 62264
14.9%
i 32673
 
7.8%
r 31542
 
7.6%
t 28248
 
6.8%
a 24698
 
5.9%
d 23550
 
5.6%
n 23319
 
5.6%
l 16877
 
4.0%
f 16786
 
4.0%
k 16583
 
4.0%
Other values (21) 140520
33.7%

position
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Defender
10401 
Midfield
9371 
Attack
8912 
Goalkeeper
3723 
Missing
 
181

Length

Max length10
Median length8
Mean length7.675985
Min length6

Characters and Unicode

Total characters250145
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttack
2nd rowGoalkeeper
3rd rowAttack
4th rowDefender
5th rowGoalkeeper

Common Values

ValueCountFrequency (%)
Defender 10401
31.9%
Midfield 9371
28.8%
Attack 8912
27.3%
Goalkeeper 3723
 
11.4%
Missing 181
 
0.6%

Length

2025-03-12T21:12:46.329754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T21:12:46.390068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
defender 10401
31.9%
midfield 9371
28.8%
attack 8912
27.3%
goalkeeper 3723
 
11.4%
missing 181
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 51743
20.7%
d 29143
11.7%
f 19772
 
7.9%
i 19104
 
7.6%
t 17824
 
7.1%
r 14124
 
5.6%
l 13094
 
5.2%
k 12635
 
5.1%
a 12635
 
5.1%
n 10582
 
4.2%
Other values (9) 49489
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 51743
20.7%
d 29143
11.7%
f 19772
 
7.9%
i 19104
 
7.6%
t 17824
 
7.1%
r 14124
 
5.6%
l 13094
 
5.2%
k 12635
 
5.1%
a 12635
 
5.1%
n 10582
 
4.2%
Other values (9) 49489
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 51743
20.7%
d 29143
11.7%
f 19772
 
7.9%
i 19104
 
7.6%
t 17824
 
7.1%
r 14124
 
5.6%
l 13094
 
5.2%
k 12635
 
5.1%
a 12635
 
5.1%
n 10582
 
4.2%
Other values (9) 49489
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 51743
20.7%
d 29143
11.7%
f 19772
 
7.9%
i 19104
 
7.6%
t 17824
 
7.1%
r 14124
 
5.6%
l 13094
 
5.2%
k 12635
 
5.1%
a 12635
 
5.1%
n 10582
 
4.2%
Other values (9) 49489
19.8%

foot
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing2540
Missing (%)7.8%
Memory size1.9 MiB
right
21132 
left
7530 
both
 
1386

Length

Max length5
Median length5
Mean length4.7032748
Min length4

Characters and Unicode

Total characters141324
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowleft
3rd rowright
4th rowboth
5th rowright

Common Values

ValueCountFrequency (%)
right 21132
64.8%
left 7530
 
23.1%
both 1386
 
4.3%
(Missing) 2540
 
7.8%

Length

2025-03-12T21:12:46.453107image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T21:12:46.505445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
right 21132
70.3%
left 7530
 
25.1%
both 1386
 
4.6%

Most occurring characters

ValueCountFrequency (%)
t 30048
21.3%
h 22518
15.9%
r 21132
15.0%
i 21132
15.0%
g 21132
15.0%
l 7530
 
5.3%
e 7530
 
5.3%
f 7530
 
5.3%
b 1386
 
1.0%
o 1386
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 30048
21.3%
h 22518
15.9%
r 21132
15.0%
i 21132
15.0%
g 21132
15.0%
l 7530
 
5.3%
e 7530
 
5.3%
f 7530
 
5.3%
b 1386
 
1.0%
o 1386
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 30048
21.3%
h 22518
15.9%
r 21132
15.0%
i 21132
15.0%
g 21132
15.0%
l 7530
 
5.3%
e 7530
 
5.3%
f 7530
 
5.3%
b 1386
 
1.0%
o 1386
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 30048
21.3%
h 22518
15.9%
r 21132
15.0%
i 21132
15.0%
g 21132
15.0%
l 7530
 
5.3%
e 7530
 
5.3%
f 7530
 
5.3%
b 1386
 
1.0%
o 1386
 
1.0%

height_in_cm
Real number (ℝ)

Missing 

Distinct52
Distinct (%)0.2%
Missing2263
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean182.29082
Minimum17
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.7 KiB
2025-03-12T21:12:46.565002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile171
Q1178
median183
Q3187
95-th percentile193
Maximum207
Range190
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.0350572
Coefficient of variation (CV)0.038592494
Kurtosis48.526418
Mean182.29082
Median Absolute Deviation (MAD)5
Skewness-2.1383809
Sum5527969
Variance49.492029
MonotonicityNot monotonic
2025-03-12T21:12:46.640106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 2187
 
6.7%
185 2031
 
6.2%
178 1789
 
5.5%
183 1757
 
5.4%
188 1550
 
4.8%
182 1521
 
4.7%
184 1491
 
4.6%
186 1470
 
4.5%
187 1434
 
4.4%
175 1405
 
4.3%
Other values (42) 13690
42.0%
(Missing) 2263
 
6.9%
ValueCountFrequency (%)
17 2
 
< 0.1%
18 2
 
< 0.1%
19 1
 
< 0.1%
159 2
 
< 0.1%
160 4
 
< 0.1%
161 3
 
< 0.1%
162 7
 
< 0.1%
163 22
 
0.1%
164 35
0.1%
165 77
0.2%
ValueCountFrequency (%)
207 1
 
< 0.1%
206 4
 
< 0.1%
205 4
 
< 0.1%
204 8
 
< 0.1%
203 11
 
< 0.1%
202 16
 
< 0.1%
201 23
 
0.1%
200 27
 
0.1%
199 29
 
0.1%
198 106
0.3%
Distinct118
Distinct (%)0.6%
Missing12098
Missing (%)37.1%
Memory size254.7 KiB
Minimum2000-05-31 00:00:00
Maximum2034-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T21:12:46.899721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:46.973721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

agent_name
Text

Missing 

Distinct2880
Distinct (%)17.4%
Missing16052
Missing (%)49.3%
Memory size1.6 MiB
2025-03-12T21:12:47.079453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length20
Median length14
Mean length11.765058
Min length2

Characters and Unicode

Total characters194547
Distinct characters106
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1148 ?
Unique (%)6.9%

Sample

1st rowASBW Sport Marketing
2nd rowNeubauer 13 GmbH
3rd rowCSKA-AS-23 Ltd.
4th rowIFM
5th rowFootball Concept
ValueCountFrequency (%)
sports 2234
 
7.1%
group 1025
 
3.3%
sport 857
 
2.7%
management 724
 
2.3%
caa 679
 
2.2%
agency 586
 
1.9%
wasserman 502
 
1.6%
football 482
 
1.5%
ltd 441
 
1.4%
soccer 419
 
1.3%
Other values (3184) 23366
74.6%
2025-03-12T21:12:47.252384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15421
 
7.9%
o 12320
 
6.3%
e 11965
 
6.2%
r 11946
 
6.1%
a 11057
 
5.7%
t 10413
 
5.4%
S 9554
 
4.9%
n 8321
 
4.3%
s 7312
 
3.8%
l 6761
 
3.5%
Other values (96) 89477
46.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15421
 
7.9%
o 12320
 
6.3%
e 11965
 
6.2%
r 11946
 
6.1%
a 11057
 
5.7%
t 10413
 
5.4%
S 9554
 
4.9%
n 8321
 
4.3%
s 7312
 
3.8%
l 6761
 
3.5%
Other values (96) 89477
46.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15421
 
7.9%
o 12320
 
6.3%
e 11965
 
6.2%
r 11946
 
6.1%
a 11057
 
5.7%
t 10413
 
5.4%
S 9554
 
4.9%
n 8321
 
4.3%
s 7312
 
3.8%
l 6761
 
3.5%
Other values (96) 89477
46.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15421
 
7.9%
o 12320
 
6.3%
e 11965
 
6.2%
r 11946
 
6.1%
a 11057
 
5.7%
t 10413
 
5.4%
S 9554
 
4.9%
n 8321
 
4.3%
s 7312
 
3.8%
l 6761
 
3.5%
Other values (96) 89477
46.0%
Distinct26826
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
https://img.a.transfermarkt.technology/portrait/header/default.jpg?lm=1
5490 
https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221
 
274
https://img.a.transfermarkt.technology/portrait/header/10-1448468291.jpg?lm=1
 
1
https://img.a.transfermarkt.technology/portrait/header/401604-1659559936.jpg?lm=1
 
1
https://img.a.transfermarkt.technology/portrait/header/401768-1561123096.jpg?lm=1
 
1
Other values (26821)
26821 
ValueCountFrequency (%)
https://img.a.transfermarkt.technology/portrait/header/default.jpg?lm=1 5490
 
16.8%
https://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221 274
 
0.8%
https://img.a.transfermarkt.technology/portrait/header/10-1448468291.jpg?lm=1 1
 
< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401604-1659559936.jpg?lm=1 1
 
< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401768-1561123096.jpg?lm=1 1
 
< 0.1%
https://tmssl.akamaized.net//images/portrait/header/401736-1680154630.jpg?lm=1680154665 1
 
< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401732-1473155527.jpg?lm=1 1
 
< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401729-1636410142.JPG?lm=1 1
 
< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401717-1533289301.jpg?lm=1 1
 
< 0.1%
https://img.a.transfermarkt.technology/portrait/header/401712-1537438711.jpg?lm=1 1
 
< 0.1%
Other values (26816) 26816
82.3%
ValueCountFrequency (%)
https 32588
100.0%
ValueCountFrequency (%)
img.a.transfermarkt.technology 26162
80.3%
tmssl.akamaized.net 6426
 
19.7%
ValueCountFrequency (%)
/portrait/header/default.jpg 5490
 
16.8%
//images/portrait/header/default.jpg 274
 
0.8%
/portrait/header/10-1448468291.jpg 1
 
< 0.1%
/portrait/header/401604-1659559936.jpg 1
 
< 0.1%
/portrait/header/401768-1561123096.jpg 1
 
< 0.1%
//images/portrait/header/401736-1680154630.jpg 1
 
< 0.1%
/portrait/header/401732-1473155527.jpg 1
 
< 0.1%
/portrait/header/401729-1636410142.JPG 1
 
< 0.1%
/portrait/header/401717-1533289301.jpg 1
 
< 0.1%
/portrait/header/401712-1537438711.jpg 1
 
< 0.1%
Other values (26816) 26816
82.3%
ValueCountFrequency (%)
lm=1 26162
80.3%
lm=1455618221 274
 
0.8%
lm=1708426123 2
 
< 0.1%
lm=1625740964 1
 
< 0.1%
lm=1723809768 1
 
< 0.1%
lm=1659947739 1
 
< 0.1%
lm=1723819820 1
 
< 0.1%
lm=1605555794 1
 
< 0.1%
lm=1711746884 1
 
< 0.1%
lm=1705056589 1
 
< 0.1%
Other values (6143) 6143
 
18.9%
ValueCountFrequency (%)
32588
100.0%

url
URL

Unique 

Distinct32588
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
https://www.transfermarkt.co.uk/miroslav-klose/profil/spieler/10
 
1
https://www.transfermarkt.co.uk/vasilios-chatziemmanouil/profil/spieler/420921
 
1
https://www.transfermarkt.co.uk/keita-endo/profil/spieler/421726
 
1
https://www.transfermarkt.co.uk/patryk-szysz/profil/spieler/421710
 
1
https://www.transfermarkt.co.uk/frank-onyeka/profil/spieler/421637
 
1
Other values (32583)
32583 
ValueCountFrequency (%)
https://www.transfermarkt.co.uk/miroslav-klose/profil/spieler/10 1
 
< 0.1%
https://www.transfermarkt.co.uk/vasilios-chatziemmanouil/profil/spieler/420921 1
 
< 0.1%
https://www.transfermarkt.co.uk/keita-endo/profil/spieler/421726 1
 
< 0.1%
https://www.transfermarkt.co.uk/patryk-szysz/profil/spieler/421710 1
 
< 0.1%
https://www.transfermarkt.co.uk/frank-onyeka/profil/spieler/421637 1
 
< 0.1%
https://www.transfermarkt.co.uk/samson-iyede/profil/spieler/421636 1
 
< 0.1%
https://www.transfermarkt.co.uk/jake-vokins/profil/spieler/421424 1
 
< 0.1%
https://www.transfermarkt.co.uk/omer-alper-tatlisu/profil/spieler/421319 1
 
< 0.1%
https://www.transfermarkt.co.uk/maksim-rudnev/profil/spieler/421284 1
 
< 0.1%
https://www.transfermarkt.co.uk/thomas-vasiliou/profil/spieler/421224 1
 
< 0.1%
Other values (32578) 32578
> 99.9%
ValueCountFrequency (%)
https 32588
100.0%
ValueCountFrequency (%)
www.transfermarkt.co.uk 32588
100.0%
ValueCountFrequency (%)
/miroslav-klose/profil/spieler/10 1
 
< 0.1%
/vasilios-chatziemmanouil/profil/spieler/420921 1
 
< 0.1%
/keita-endo/profil/spieler/421726 1
 
< 0.1%
/patryk-szysz/profil/spieler/421710 1
 
< 0.1%
/frank-onyeka/profil/spieler/421637 1
 
< 0.1%
/samson-iyede/profil/spieler/421636 1
 
< 0.1%
/jake-vokins/profil/spieler/421424 1
 
< 0.1%
/omer-alper-tatlisu/profil/spieler/421319 1
 
< 0.1%
/maksim-rudnev/profil/spieler/421284 1
 
< 0.1%
/thomas-vasiliou/profil/spieler/421224 1
 
< 0.1%
Other values (32578) 32578
> 99.9%
ValueCountFrequency (%)
32588
100.0%
ValueCountFrequency (%)
32588
100.0%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
TR1
3220 
IT1
3177 
PO1
2660 
GR1
2604 
FR1
2246 
Other values (9)
18681 

Length

Max length4
Median length3
Mean length3.0054314
Min length2

Characters and Unicode

Total characters97941
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIT1
2nd rowL1
3rd rowGR1
4th rowIT1
5th rowL1

Common Values

ValueCountFrequency (%)
TR1 3220
9.9%
IT1 3177
9.7%
PO1 2660
 
8.2%
GR1 2604
 
8.0%
FR1 2246
 
6.9%
NL1 2229
 
6.8%
ES1 2215
 
6.8%
BE1 2214
 
6.8%
GB1 2180
 
6.7%
RU1 2180
 
6.7%
Other values (4) 7663
23.5%

Length

2025-03-12T21:12:47.323028image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tr1 3220
9.9%
it1 3177
9.7%
po1 2660
 
8.2%
gr1 2604
 
8.0%
fr1 2246
 
6.9%
nl1 2229
 
6.8%
es1 2215
 
6.8%
be1 2214
 
6.8%
gb1 2180
 
6.7%
ru1 2180
 
6.7%
Other values (4) 7663
23.5%

Most occurring characters

ValueCountFrequency (%)
1 32588
33.3%
R 12302
 
12.6%
T 6397
 
6.5%
G 4784
 
4.9%
E 4429
 
4.5%
B 4394
 
4.5%
S 4237
 
4.3%
U 4232
 
4.3%
L 4104
 
4.2%
K 3766
 
3.8%
Other values (7) 16708
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 32588
33.3%
R 12302
 
12.6%
T 6397
 
6.5%
G 4784
 
4.9%
E 4429
 
4.5%
B 4394
 
4.5%
S 4237
 
4.3%
U 4232
 
4.3%
L 4104
 
4.2%
K 3766
 
3.8%
Other values (7) 16708
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 32588
33.3%
R 12302
 
12.6%
T 6397
 
6.5%
G 4784
 
4.9%
E 4429
 
4.5%
B 4394
 
4.5%
S 4237
 
4.3%
U 4232
 
4.3%
L 4104
 
4.2%
K 3766
 
3.8%
Other values (7) 16708
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 32588
33.3%
R 12302
 
12.6%
T 6397
 
6.5%
G 4784
 
4.9%
E 4429
 
4.5%
B 4394
 
4.5%
S 4237
 
4.3%
U 4232
 
4.3%
L 4104
 
4.2%
K 3766
 
3.8%
Other values (7) 16708
17.1%
Distinct437
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2025-03-12T21:12:47.456771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length98
Median length40
Mean length21.877071
Min length4

Characters and Unicode

Total characters712930
Distinct characters111
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSocietà Sportiva Lazio S.p.A.
2nd rowBorussia Dortmund
3rd rowPanthessalonikios Athlitikos Omilos Konstantinoupoliton
4th rowJuventus Football Club
5th rowFC Bayern München
ValueCountFrequency (%)
club 7817
 
7.7%
football 5227
 
5.1%
fc 2468
 
2.4%
de 2180
 
2.1%
kulübü 1411
 
1.4%
clube 1387
 
1.4%
fk 1321
 
1.3%
calcio 1256
 
1.2%
s.a.d 1125
 
1.1%
koninklijke 858
 
0.8%
Other values (772) 76539
75.3%
2025-03-12T21:12:47.670408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69001
 
9.7%
o 51907
 
7.3%
l 49996
 
7.0%
e 48588
 
6.8%
a 48400
 
6.8%
i 38245
 
5.4%
n 34273
 
4.8%
r 33727
 
4.7%
t 33000
 
4.6%
b 25431
 
3.6%
Other values (101) 280362
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
69001
 
9.7%
o 51907
 
7.3%
l 49996
 
7.0%
e 48588
 
6.8%
a 48400
 
6.8%
i 38245
 
5.4%
n 34273
 
4.8%
r 33727
 
4.7%
t 33000
 
4.6%
b 25431
 
3.6%
Other values (101) 280362
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
69001
 
9.7%
o 51907
 
7.3%
l 49996
 
7.0%
e 48588
 
6.8%
a 48400
 
6.8%
i 38245
 
5.4%
n 34273
 
4.8%
r 33727
 
4.7%
t 33000
 
4.6%
b 25431
 
3.6%
Other values (101) 280362
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
69001
 
9.7%
o 51907
 
7.3%
l 49996
 
7.0%
e 48588
 
6.8%
a 48400
 
6.8%
i 38245
 
5.4%
n 34273
 
4.8%
r 33727
 
4.7%
t 33000
 
4.6%
b 25431
 
3.6%
Other values (101) 280362
39.3%

market_value_in_eur
Real number (ℝ)

High correlation  Missing 

Distinct128
Distinct (%)0.4%
Missing1564
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean1619112
Minimum10000
Maximum2 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.7 KiB
2025-03-12T21:12:47.740328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile25000
Q1100000
median250000
Q3700000
95-th percentile7000000
Maximum2 × 108
Range1.9999 × 108
Interquartile range (IQR)600000

Descriptive statistics

Standard deviation6359597.7
Coefficient of variation (CV)3.9278307
Kurtosis208.47248
Mean1619112
Median Absolute Deviation (MAD)175000
Skewness11.400756
Sum5.023133 × 1010
Variance4.0444483 × 1013
MonotonicityNot monotonic
2025-03-12T21:12:47.808385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 3036
 
9.3%
50000 2900
 
8.9%
200000 2518
 
7.7%
150000 2152
 
6.6%
300000 1892
 
5.8%
250000 1750
 
5.4%
25000 1325
 
4.1%
75000 1163
 
3.6%
400000 1151
 
3.5%
500000 1132
 
3.5%
Other values (118) 12005
36.8%
(Missing) 1564
 
4.8%
ValueCountFrequency (%)
10000 229
 
0.7%
20000 2
 
< 0.1%
25000 1325
4.1%
50000 2900
8.9%
75000 1163
 
3.6%
100000 3036
9.3%
125000 681
 
2.1%
150000 2152
6.6%
175000 486
 
1.5%
200000 2518
7.7%
ValueCountFrequency (%)
200000000 2
< 0.1%
180000000 2
< 0.1%
160000000 1
 
< 0.1%
150000000 1
 
< 0.1%
140000000 3
< 0.1%
130000000 3
< 0.1%
110000000 2
< 0.1%
100000000 3
< 0.1%
90000000 1
 
< 0.1%
85000000 2
< 0.1%

highest_market_value_in_eur
Real number (ℝ)

High correlation  Missing 

Distinct205
Distinct (%)0.7%
Missing1564
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean3736375.7
Minimum10000
Maximum2 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size254.7 KiB
2025-03-12T21:12:47.875235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile50000
Q1275000
median800000
Q33000000
95-th percentile18000000
Maximum2 × 108
Range1.9999 × 108
Interquartile range (IQR)2725000

Descriptive statistics

Standard deviation9784711.2
Coefficient of variation (CV)2.6187707
Kurtosis76.841006
Mean3736375.7
Median Absolute Deviation (MAD)675000
Skewness7.0757113
Sum1.1591732 × 1011
Variance9.5740572 × 1013
MonotonicityNot monotonic
2025-03-12T21:12:47.943003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 1386
 
4.3%
100000 1380
 
4.2%
50000 1377
 
4.2%
1000000 1352
 
4.1%
500000 1324
 
4.1%
200000 1288
 
4.0%
400000 1250
 
3.8%
150000 1115
 
3.4%
1500000 1114
 
3.4%
2000000 1060
 
3.3%
Other values (195) 18378
56.4%
(Missing) 1564
 
4.8%
ValueCountFrequency (%)
10000 44
 
0.1%
25000 318
 
1.0%
50000 1377
4.2%
75000 512
 
1.6%
100000 1380
4.2%
125000 289
 
0.9%
150000 1115
3.4%
175000 225
 
0.7%
200000 1288
4.0%
225000 146
 
0.4%
ValueCountFrequency (%)
200000000 3
 
< 0.1%
180000000 4
 
< 0.1%
160000000 1
 
< 0.1%
150000000 9
< 0.1%
140000000 2
 
< 0.1%
130000000 4
 
< 0.1%
120000000 4
 
< 0.1%
110000000 5
 
< 0.1%
100000000 15
< 0.1%
90000000 20
0.1%

Interactions

2025-03-12T21:12:41.667772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:39.949548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.433070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.749961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.035839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.346667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.725931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.092129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.487376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.797861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.083956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.395488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.779842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.194572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.537908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.843899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.132097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.444300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.831591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.252724image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.590270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.886629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.177422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.491077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.889820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.302177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.641632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.935127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.231813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.552291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.946115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.355400image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.696358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:40.983858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.293197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T21:12:41.605829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-12T21:12:47.993176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
current_club_domestic_competition_idcurrent_club_idfootheight_in_cmhighest_market_value_in_eurlast_seasonmarket_value_in_eurplayer_idpositionsub_position
current_club_domestic_competition_id1.0000.2200.0790.0540.0830.0430.0710.0700.0410.051
current_club_id0.2201.0000.009-0.035-0.185-0.037-0.1940.0240.0120.006
foot0.0790.0091.0000.0460.0000.0430.0000.0400.1250.355
height_in_cm0.054-0.0350.0461.000-0.0250.073-0.0080.0150.2340.297
highest_market_value_in_eur0.083-0.1850.000-0.0251.0000.2540.692-0.3520.0310.023
last_season0.043-0.0370.0430.0730.2541.0000.5260.5630.0340.027
market_value_in_eur0.071-0.1940.000-0.0080.6920.5261.0000.1420.0170.011
player_id0.0700.0240.0400.015-0.3520.5630.1421.0000.0250.033
position0.0410.0120.1250.2340.0310.0340.0170.0251.0001.000
sub_position0.0510.0060.3550.2970.0230.0270.0110.0331.0001.000

Missing values

2025-03-12T21:12:42.111747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T21:12:42.280815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-12T21:12:42.459968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

player_idfirst_namelast_namenamelast_seasoncurrent_club_idplayer_codecountry_of_birthcity_of_birthcountry_of_citizenshipdate_of_birthsub_positionpositionfootheight_in_cmcontract_expiration_dateagent_nameimage_urlurlcurrent_club_domestic_competition_idcurrent_club_namemarket_value_in_eurhighest_market_value_in_eur
010MiroslavKloseMiroslav Klose2015398miroslav-klosePolandOpoleGermany1978-06-09 00:00:00Centre-ForwardAttackright184.0NaNASBW Sport Marketinghttps://img.a.transfermarkt.technology/portrait/header/10-1448468291.jpg?lm=1https://www.transfermarkt.co.uk/miroslav-klose/profil/spieler/10IT1Società Sportiva Lazio S.p.A.1000000.030000000.0
126RomanWeidenfellerRoman Weidenfeller201716roman-weidenfellerGermanyDiezGermany1980-08-06 00:00:00GoalkeeperGoalkeeperleft190.0NaNNeubauer 13 GmbHhttps://img.a.transfermarkt.technology/portrait/header/26-1502448725.jpg?lm=1https://www.transfermarkt.co.uk/roman-weidenfeller/profil/spieler/26L1Borussia Dortmund750000.08000000.0
265DimitarBerbatovDimitar Berbatov20151091dimitar-berbatovBulgariaBlagoevgradBulgaria1981-01-30 00:00:00Centre-ForwardAttackNaNNaNNaNCSKA-AS-23 Ltd.https://img.a.transfermarkt.technology/portrait/header/65-1683670068.jpg?lm=1https://www.transfermarkt.co.uk/dimitar-berbatov/profil/spieler/65GR1Panthessalonikios Athlitikos Omilos Konstantinoupoliton1000000.034500000.0
377NaNLúcioLúcio2012506lucioBrazilBrasíliaBrazil1978-05-08 00:00:00Centre-BackDefenderNaNNaNNaNNaNhttps://img.a.transfermarkt.technology/portrait/header/77-1458201664.jpg?lm=1https://www.transfermarkt.co.uk/lucio/profil/spieler/77IT1Juventus Football Club200000.024500000.0
480TomStarkeTom Starke201727tom-starkeEast Germany (GDR)FreitalGermany1981-03-18 00:00:00GoalkeeperGoalkeeperright194.0NaNIFMhttps://img.a.transfermarkt.technology/portrait/header/80-1471439914.JPG?lm=1https://www.transfermarkt.co.uk/tom-starke/profil/spieler/80L1FC Bayern München100000.03000000.0
5109NaNDedêDedê2013825dedeBrazilBelo HorizonteBrazil1978-04-18 00:00:00Left-BackDefenderNaNNaNNaNFootball Concepthttps://img.a.transfermarkt.technology/portrait/header/109-1487263703.jpg?lm=1https://www.transfermarkt.co.uk/dede/profil/spieler/109TR1Eskisehirspor400000.09500000.0
6123ChristophMetzelderChristoph Metzelder201233christoph-metzelderGermanyHalternGermany1980-11-05 00:00:00Centre-BackDefenderNaNNaNNaNNaNhttps://img.a.transfermarkt.technology/portrait/header/s_123_33_2012_1.jpg?lm=1https://www.transfermarkt.co.uk/christoph-metzelder/profil/spieler/123L1FC Schalke 041500000.09500000.0
7132TomasRosickyTomas Rosicky201511tomas-rosickyCSSRPrahaCzech Republic1980-10-04 00:00:00Attacking MidfieldMidfieldboth179.0NaNNaNhttps://img.a.transfermarkt.technology/portrait/header/132-1464947592.jpg?lm=1https://www.transfermarkt.co.uk/tomas-rosicky/profil/spieler/132GB1Arsenal Football Club350000.017500000.0
8162MarcZieglerMarc Ziegler201279marc-zieglerGermanyBlieskastelGermany1976-06-13 00:00:00GoalkeeperGoalkeeperright193.0NaNNaNhttps://img.a.transfermarkt.technology/portrait/header/s_162_79_2012_1.jpg?lm=1https://www.transfermarkt.co.uk/marc-ziegler/profil/spieler/162L1Verein für Bewegungsspiele Stuttgart 1893200000.01250000.0
9215RoqueSanta CruzRoque Santa Cruz20151084roque-santa-cruzParaguayAsunciónParaguay1981-08-16 00:00:00Centre-ForwardAttackright193.02023-12-31 00:00:00NaNhttps://img.a.transfermarkt.technology/portrait/header/215-1564992591.png?lm=1https://www.transfermarkt.co.uk/roque-santa-cruz/profil/spieler/215ES1Málaga CF250000.012000000.0
player_idfirst_namelast_namenamelast_seasoncurrent_club_idplayer_codecountry_of_birthcity_of_birthcountry_of_citizenshipdate_of_birthsub_positionpositionfootheight_in_cmcontract_expiration_dateagent_nameimage_urlurlcurrent_club_domestic_competition_idcurrent_club_namemarket_value_in_eurhighest_market_value_in_eur
325781358447KyanHimpeKyan Himpe2024601kyan-himpeNaNNaNBelgium2006-04-03 00:00:00Centre-ForwardAttackNaNNaN2027-06-30 00:00:00NaNhttps://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221https://www.transfermarkt.co.uk/kyan-himpe/profil/spieler/1358447BE1Koninklijke Voetbalclub KortrijkNaNNaN
325791358448FuhnaWirsiy NsoloFuhna Wirsiy Nsolo2024601fuhna-wirsiy-nsoloNaNNaNBelgium2005-04-26 00:00:00Centre-BackDefenderNaNNaN2027-06-30 00:00:00NaNhttps://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221https://www.transfermarkt.co.uk/fuhna-wirsiy-nsolo/profil/spieler/1358448BE1Koninklijke Voetbalclub KortrijkNaNNaN
325801364960DarrylNkulikiyimanaDarryl Nkulikiyimana20249010darryl-nkulikiyimanaNaNNaNBelgium2005-05-24 00:00:00Centre-BackDefenderleftNaNNaNNaNhttps://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221https://www.transfermarkt.co.uk/darryl-nkulikiyimana/profil/spieler/1364960BE1FC Verbroedering Denderhoutem Denderleeuw Eendracht HekelgemNaNNaN
325811365020AfonsoAssisAfonso Assis2024979afonso-assisNaNNaNPortugal2006-07-15 00:00:00Defensive MidfieldMidfieldleftNaN2025-06-30 00:00:00NaNhttps://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221https://www.transfermarkt.co.uk/afonso-assis/profil/spieler/1365020PO1Moreirense Futebol ClubeNaNNaN
325821367128AbdoulayeYoroAbdoulaye Yoro20246890abdoulaye-yoroNaNNaNCote d'Ivoire2006-12-12 00:00:00Attacking MidfieldMidfieldleft175.02027-06-30 00:00:00Tamegnon Consultinghttps://tmssl.akamaized.net//images/portrait/header/1367128-1739874937.png?lm=1739875017https://www.transfermarkt.co.uk/abdoulaye-yoro/profil/spieler/1367128TR1İstanbul Başakşehir Futbol KulübüNaNNaN
325831369057YusufKurtYusuf Kurt202411282yusuf-kurtTürkiyeBatmanTürkiye2002-05-07 00:00:00Attacking MidfieldMidfieldleft186.02025-06-30 00:00:00NaNhttps://tmssl.akamaized.net//images/portrait/header/1369057-1741084226.jpg?lm=1741084233https://www.transfermarkt.co.uk/yusuf-kurt/profil/spieler/1369057TR1AlanyasporNaNNaN
325841375876NaNDiego HenriqueDiego Henrique202486209diego-henriqueBrazilAndradina, SPBrazil2003-08-23 00:00:00Attacking MidfieldMidfieldleft170.02026-06-30 00:00:00NaNhttps://tmssl.akamaized.net//images/portrait/header/1375876-1739627135.png?lm=1739627187https://www.transfermarkt.co.uk/diego-henrique/profil/spieler/1375876UKR1FK Livyi BerehNaNNaN
325851378362OrseerAchihiOrseer Achihi20241096orseer-achihiNaNNaNNigeria2006-09-19 00:00:00Left WingerAttackNaNNaN2029-06-30 00:00:00Aneke/PMGhttps://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221https://www.transfermarkt.co.uk/orseer-achihi/profil/spieler/1378362BE1Royal Antwerp Football ClubNaNNaN
325861380311Prince AmoakoJuniorPrince Amoako Junior20242778prince-amoako-juniorNaNNaNGhana2007-02-19 00:00:00Left WingerAttackNaNNaN2029-12-31 00:00:00CAA Stellarhttps://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221https://www.transfermarkt.co.uk/prince-amoako-junior/profil/spieler/1380311DK1Fodbold Club NordsjællandNaNNaN
325871380876Gabriel JesusDavidGabriel Jesus David20241096gabriel-jesus-davidNaNNaNNigeria2007-01-02 00:00:00Centre-ForwardAttackNaNNaN2025-06-30 00:00:00Aneke/PMGhttps://tmssl.akamaized.net//images/portrait/header/default.jpg?lm=1455618221https://www.transfermarkt.co.uk/gabriel-jesus-david/profil/spieler/1380876BE1Royal Antwerp Football ClubNaNNaN